Overview

Dataset statistics

Number of variables17
Number of observations4154739
Missing cells13024555
Missing cells (%)18.4%
Duplicate rows13752
Duplicate rows (%)0.3%
Total size in memory2.0 GiB
Average record size in memory517.0 B

Variable types

Numeric7
DateTime2
Unsupported2
Text3
Categorical3

Alerts

Dataset has 13752 (0.3%) duplicate rowsDuplicates
start_time has 1092248 (26.3%) missing valuesMissing
stop_time has 1092248 (26.3%) missing valuesMissing
bike_id has 2483972 (59.8%) missing valuesMissing
birth_year has 2524704 (60.8%) missing valuesMissing
gender has 2483972 (59.8%) missing valuesMissing
ride_id has 1670767 (40.2%) missing valuesMissing
rideable_type has 1670767 (40.2%) missing valuesMissing
start_station_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
end_station_id is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-01-21 20:14:47.599019
Analysis finished2024-01-21 20:18:15.390988
Duration3 minutes and 27.79 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

trip_duration
Real number (ℝ)

Distinct5271
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.74895
Minimum1
Maximum5271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 MiB
2024-01-21T15:18:15.558448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile127
Q1254
median404
Q3693
95-th percentile2005
Maximum5271
Range5270
Interquartile range (IQR)439

Descriptive statistics

Standard deviation678.04054
Coefficient of variation (CV)1.0835664
Kurtosis11.592739
Mean625.74895
Median Absolute Deviation (MAD)183
Skewness3.0543369
Sum2.5998236 × 109
Variance459738.98
MonotonicityNot monotonic
2024-01-21T15:18:15.791147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
248 8236
 
0.2%
252 8224
 
0.2%
256 8205
 
0.2%
244 8200
 
0.2%
253 8177
 
0.2%
241 8173
 
0.2%
246 8168
 
0.2%
251 8155
 
0.2%
249 8155
 
0.2%
243 8152
 
0.2%
Other values (5261) 4072894
98.0%
ValueCountFrequency (%)
1 889
< 0.1%
2 1840
< 0.1%
3 1558
< 0.1%
4 1149
< 0.1%
5 1050
< 0.1%
6 964
< 0.1%
7 1015
< 0.1%
8 950
< 0.1%
9 891
< 0.1%
10 860
< 0.1%
ValueCountFrequency (%)
5271 19
< 0.1%
5270 16
< 0.1%
5269 13
< 0.1%
5268 13
< 0.1%
5267 9
< 0.1%
5266 17
< 0.1%
5265 17
< 0.1%
5264 12
< 0.1%
5263 18
< 0.1%
5262 11
< 0.1%

start_time
Date

MISSING 

Distinct2987105
Distinct (%)97.5%
Missing1092248
Missing (%)26.3%
Memory size63.4 MiB
Minimum2015-09-21 14:53:16
Maximum2023-12-31 23:59:57
2024-01-21T15:18:16.064026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:18:16.320671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stop_time
Date

MISSING 

Distinct2989726
Distinct (%)97.6%
Missing1092248
Missing (%)26.3%
Memory size63.4 MiB
Minimum2015-09-21 14:54:17
Maximum2024-01-01 00:28:15
2024-01-21T15:18:16.520308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:18:16.738775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

start_station_id
Unsupported

REJECTED  UNSUPPORTED 

Missing87
Missing (%)< 0.1%
Memory size254.6 MiB
Distinct319
Distinct (%)< 0.1%
Missing87
Missing (%)< 0.1%
Memory size324.4 MiB
2024-01-21T15:18:17.159523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length44
Median length38
Mean length16.871595
Min length4

Characters and Unicode

Total characters70095604
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)< 0.1%

Sample

1st rowMarin Light Rail
2nd rowExchange Place
3rd rowExchange Place
4th rowMcGinley Square
5th rowExchange Place
ValueCountFrequency (%)
st 2463073
 
16.7%
1854592
 
12.6%
ave 491720
 
3.3%
park 477837
 
3.2%
path 430822
 
2.9%
grove 296914
 
2.0%
hudson 283686
 
1.9%
newport 256467
 
1.7%
rail 241798
 
1.6%
light 241798
 
1.6%
Other values (313) 7674110
52.2%
2024-01-21T15:18:17.918308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10558167
 
15.1%
t 5061636
 
7.2%
r 4169273
 
5.9%
a 4148751
 
5.9%
e 3767796
 
5.4%
o 3566946
 
5.1%
n 3556869
 
5.1%
S 3066563
 
4.4%
i 3035366
 
4.3%
l 2436196
 
3.5%
Other values (53) 26728041
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43206494
61.6%
Uppercase Letter 12987092
 
18.5%
Space Separator 10558167
 
15.1%
Decimal Number 1489253
 
2.1%
Other Punctuation 1330824
 
1.9%
Dash Punctuation 523772
 
0.7%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 5061636
11.7%
r 4169273
9.6%
a 4148751
9.6%
e 3767796
 
8.7%
o 3566946
 
8.3%
n 3556869
 
8.2%
i 3035366
 
7.0%
l 2436196
 
5.6%
s 1849751
 
4.3%
k 1355919
 
3.1%
Other values (15) 10257991
23.7%
Uppercase Letter
ValueCountFrequency (%)
S 3066563
23.6%
H 1542175
11.9%
P 1471505
11.3%
A 985173
 
7.6%
M 712580
 
5.5%
C 689331
 
5.3%
T 626203
 
4.8%
G 531651
 
4.1%
L 521317
 
4.0%
W 503835
 
3.9%
Other values (13) 2336759
18.0%
Decimal Number
ValueCountFrequency (%)
1 607167
40.8%
4 198586
 
13.3%
6 196254
 
13.2%
2 127049
 
8.5%
5 82241
 
5.5%
8 72028
 
4.8%
9 64147
 
4.3%
7 60661
 
4.1%
3 57331
 
3.8%
0 23789
 
1.6%
Space Separator
ValueCountFrequency (%)
10558167
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1330824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 523772
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56193586
80.2%
Common 13902018
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 5061636
 
9.0%
r 4169273
 
7.4%
a 4148751
 
7.4%
e 3767796
 
6.7%
o 3566946
 
6.3%
n 3556869
 
6.3%
S 3066563
 
5.5%
i 3035366
 
5.4%
l 2436196
 
4.3%
s 1849751
 
3.3%
Other values (38) 21534439
38.3%
Common
ValueCountFrequency (%)
10558167
75.9%
& 1330824
 
9.6%
1 607167
 
4.4%
- 523772
 
3.8%
4 198586
 
1.4%
6 196254
 
1.4%
2 127049
 
0.9%
5 82241
 
0.6%
8 72028
 
0.5%
9 64147
 
0.5%
Other values (5) 141783
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70095604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10558167
 
15.1%
t 5061636
 
7.2%
r 4169273
 
5.9%
a 4148751
 
5.9%
e 3767796
 
5.4%
o 3566946
 
5.1%
n 3556869
 
5.1%
S 3066563
 
4.4%
i 3035366
 
4.3%
l 2436196
 
3.5%
Other values (53) 26728041
38.1%

start_station_latitude
Real number (ℝ)

Distinct128281
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728069
Minimum40.678334
Maximum40.806619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 MiB
2024-01-21T15:18:18.134150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40.678334
5-th percentile40.712774
Q140.719252
median40.725685
Q340.736982
95-th percentile40.749985
Maximum40.806619
Range0.12828463
Interquartile range (IQR)0.017730518

Descriptive statistics

Standard deviation0.01141219
Coefficient of variation (CV)0.00028020455
Kurtosis-0.65359517
Mean40.728069
Median Absolute Deviation (MAD)0.0079534836
Skewness0.5754121
Sum1.692145 × 108
Variance0.00013023809
MonotonicityNot monotonic
2024-01-21T15:18:18.342921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71958612 238391
 
5.7%
40.72759597 142834
 
3.4%
40.7272235 119577
 
2.9%
40.7287448 109393
 
2.6%
40.71458404 90710
 
2.2%
40.7192517 79290
 
1.9%
40.7177325 78799
 
1.9%
40.7211236 77973
 
1.9%
40.7112423 74476
 
1.8%
40.72152515 73676
 
1.8%
Other values (128271) 3069620
73.9%
ValueCountFrequency (%)
40.67833407 1
 
< 0.1%
40.69187865 1
 
< 0.1%
40.69263997 304
< 0.1%
40.69531906 1
 
< 0.1%
40.6970299 507
< 0.1%
40.69865054 607
< 0.1%
40.69967915 1
 
< 0.1%
40.70068073 1
 
< 0.1%
40.70362493 1
 
< 0.1%
40.70452571 1
 
< 0.1%
ValueCountFrequency (%)
40.8066187 1
 
< 0.1%
40.8028376 1
 
< 0.1%
40.79953315 1
 
< 0.1%
40.79118803 1
 
< 0.1%
40.78903018 9
< 0.1%
40.78179272 1
 
< 0.1%
40.78096507 1
 
< 0.1%
40.77986812 2
 
< 0.1%
40.77917727 1
 
< 0.1%
40.77564173 1
 
< 0.1%

start_station_longitude
Real number (ℝ)

Distinct136378
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.042824
Minimum-74.096937
Maximum-73.997279
Zeros0
Zeros (%)0.0%
Negative4154739
Negative (%)100.0%
Memory size63.4 MiB
2024-01-21T15:18:18.530898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-74.096937
5-th percentile-74.067104
Q1-74.04879
median-74.042817
Q3-74.033552
95-th percentile-74.027781
Maximum-73.997279
Range0.099658033
Interquartile range (IQR)0.015238436

Descriptive statistics

Standard deviation0.012030775
Coefficient of variation (CV)-0.00016248402
Kurtosis0.52153601
Mean-74.042824
Median Absolute Deviation (MAD)0.0081729438
Skewness-0.92486155
Sum-3.0762861 × 108
Variance0.00014473956
MonotonicityNot monotonic
2024-01-21T15:18:18.716105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.04311746 238391
 
5.7%
-74.04424731 142834
 
3.4%
-74.0337589 119577
 
2.9%
-74.0321082 109393
 
2.6%
-74.04281706 90710
 
2.2%
-74.034234 83288
 
2.0%
-74.043845 82173
 
2.0%
-74.03805095 77973
 
1.9%
-74.0557013 74476
 
1.8%
-74.04630454 73676
 
1.8%
Other values (136368) 3062248
73.7%
ValueCountFrequency (%)
-74.0969366 507
< 0.1%
-74.08896387 2
 
< 0.1%
-74.0887723 689
< 0.1%
-74.08801228 304
< 0.1%
-74.08722293 1
 
< 0.1%
-74.08706903 1
 
< 0.1%
-74.0868541 1
 
< 0.1%
-74.08684921 1
 
< 0.1%
-74.08684228 1
 
< 0.1%
-74.08682585 1
 
< 0.1%
ValueCountFrequency (%)
-73.99727857 1
 
< 0.1%
-73.99793313 9
< 0.1%
-73.99886102 1
 
< 0.1%
-74.00098978 2
 
< 0.1%
-74.00336778 1
 
< 0.1%
-74.00455577 1
 
< 0.1%
-74.00700725 1
 
< 0.1%
-74.00907335 1
 
< 0.1%
-74.01260012 4
< 0.1%
-74.0140628 1
 
< 0.1%

end_station_id
Unsupported

REJECTED  UNSUPPORTED 

Missing2608
Missing (%)0.1%
Memory size254.6 MiB
Distinct586
Distinct (%)< 0.1%
Missing2608
Missing (%)0.1%
Memory size324.5 MiB
2024-01-21T15:18:19.043299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length55
Median length40
Mean length16.924165
Min length4

Characters and Unicode

Total characters70271349
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)< 0.1%

Sample

1st rowCity Hall
2nd rowHeights Elevator
3rd rowNewark Ave
4th rowDanforth Light Rail
5th rowHamilton Park
ValueCountFrequency (%)
st 2524756
 
17.1%
1855311
 
12.5%
path 487486
 
3.3%
park 469073
 
3.2%
ave 467573
 
3.2%
grove 350723
 
2.4%
hudson 284030
 
1.9%
newport 259493
 
1.8%
pl 246014
 
1.7%
rail 245556
 
1.7%
Other values (415) 7611810
51.4%
2024-01-21T15:18:19.613327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10649710
 
15.2%
t 5101326
 
7.3%
r 4143604
 
5.9%
a 4123322
 
5.9%
e 3775432
 
5.4%
o 3587533
 
5.1%
n 3522114
 
5.0%
S 3109347
 
4.4%
i 2976355
 
4.2%
l 2408860
 
3.4%
Other values (56) 26873746
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43033503
61.2%
Uppercase Letter 13245182
 
18.8%
Space Separator 10649710
 
15.2%
Decimal Number 1486988
 
2.1%
Other Punctuation 1328776
 
1.9%
Dash Punctuation 527163
 
0.8%
Close Punctuation 13
 
< 0.1%
Open Punctuation 13
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 5101326
11.9%
r 4143604
9.6%
a 4123322
9.6%
e 3775432
 
8.8%
o 3587533
 
8.3%
n 3522114
 
8.2%
i 2976355
 
6.9%
l 2408860
 
5.6%
s 1817937
 
4.2%
k 1336023
 
3.1%
Other values (15) 10240997
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 3109347
23.5%
H 1588366
12.0%
P 1537133
11.6%
A 1018401
 
7.7%
M 691193
 
5.2%
C 687070
 
5.2%
T 682458
 
5.2%
G 567973
 
4.3%
L 529544
 
4.0%
W 509125
 
3.8%
Other values (13) 2324572
17.6%
Decimal Number
ValueCountFrequency (%)
1 617125
41.5%
4 201283
 
13.5%
6 182305
 
12.3%
2 128816
 
8.7%
5 79211
 
5.3%
8 71700
 
4.8%
9 64998
 
4.4%
7 61398
 
4.1%
3 54841
 
3.7%
0 25311
 
1.7%
Other Punctuation
ValueCountFrequency (%)
& 1328166
> 99.9%
' 596
 
< 0.1%
\ 14
 
< 0.1%
Space Separator
ValueCountFrequency (%)
10649710
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 527163
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56278685
80.1%
Common 13992664
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 5101326
 
9.1%
r 4143604
 
7.4%
a 4123322
 
7.3%
e 3775432
 
6.7%
o 3587533
 
6.4%
n 3522114
 
6.3%
S 3109347
 
5.5%
i 2976355
 
5.3%
l 2408860
 
4.3%
s 1817937
 
3.2%
Other values (38) 21712855
38.6%
Common
ValueCountFrequency (%)
10649710
76.1%
& 1328166
 
9.5%
1 617125
 
4.4%
- 527163
 
3.8%
4 201283
 
1.4%
6 182305
 
1.3%
2 128816
 
0.9%
5 79211
 
0.6%
8 71700
 
0.5%
9 64998
 
0.5%
Other values (8) 142187
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70271349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10649710
 
15.2%
t 5101326
 
7.3%
r 4143604
 
5.9%
a 4123322
 
5.9%
e 3775432
 
5.4%
o 3587533
 
5.1%
n 3522114
 
5.0%
S 3109347
 
4.4%
i 2976355
 
4.2%
l 2408860
 
3.4%
Other values (56) 26873746
38.2%

end_station_latitude
Real number (ℝ)

Distinct683
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.72787
Minimum40.64507
Maximum40.832164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 MiB
2024-01-21T15:18:19.786153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40.64507
5-th percentile40.712774
Q140.719252
median40.72534
Q340.736068
95-th percentile40.749985
Maximum40.832164
Range0.187094
Interquartile range (IQR)0.016815956

Descriptive statistics

Standard deviation0.011485306
Coefficient of variation (CV)0.00028200114
Kurtosis-0.60761009
Mean40.72787
Median Absolute Deviation (MAD)0.0076074254
Skewness0.61049202
Sum1.6921367 × 108
Variance0.00013191224
MonotonicityNot monotonic
2024-01-21T15:18:19.969186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71958612 297863
 
7.2%
40.72759597 152564
 
3.7%
40.7272235 136301
 
3.3%
40.7287448 122024
 
2.9%
40.71458404 99974
 
2.4%
40.73606766 91852
 
2.2%
40.7177325 91630
 
2.2%
40.73698222 90033
 
2.2%
40.7192517 88715
 
2.1%
40.7112423 87852
 
2.1%
Other values (673) 2895931
69.7%
ValueCountFrequency (%)
40.64507 1
< 0.1%
40.646377 1
< 0.1%
40.649292 1
< 0.1%
40.64958 1
< 0.1%
40.6630619 1
< 0.1%
40.6663181 1
< 0.1%
40.6679411 1
< 0.1%
40.66967 1
< 0.1%
40.67 1
< 0.1%
40.6704922 2
< 0.1%
ValueCountFrequency (%)
40.832164 1
 
< 0.1%
40.8120562 1
 
< 0.1%
40.8082 4
< 0.1%
40.8078316 1
 
< 0.1%
40.8067581 4
< 0.1%
40.805973 1
 
< 0.1%
40.804213 3
< 0.1%
40.802692 1
 
< 0.1%
40.801694 1
 
< 0.1%
40.8008363 1
 
< 0.1%

end_station_longitude
Real number (ℝ)

Distinct682
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.042467
Minimum-74.11
Maximum-73.948603
Zeros0
Zeros (%)0.0%
Negative4154739
Negative (%)100.0%
Memory size63.4 MiB
2024-01-21T15:18:20.147096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-74.11
5-th percentile-74.066611
Q1-74.047727
median-74.042817
Q3-74.033552
95-th percentile-74.027781
Maximum-73.948603
Range0.16139706
Interquartile range (IQR)0.014174725

Descriptive statistics

Standard deviation0.011955561
Coefficient of variation (CV)-0.00016146897
Kurtosis0.96656422
Mean-74.042467
Median Absolute Deviation (MAD)0.0078393438
Skewness-0.9367918
Sum-3.0762713 × 108
Variance0.00014293543
MonotonicityNot monotonic
2024-01-21T15:18:20.328373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.04311746 297863
 
7.2%
-74.04424731 152564
 
3.7%
-74.0337589 136301
 
3.3%
-74.0321082 122024
 
2.9%
-74.04281706 99974
 
2.4%
-74.043845 92074
 
2.2%
-74.02912706 91852
 
2.2%
-74.02778059 90033
 
2.2%
-74.034234 89193
 
2.1%
-74.0557013 87852
 
2.1%
Other values (672) 2895009
69.7%
ValueCountFrequency (%)
-74.11 3
 
< 0.1%
-74.1 9
 
< 0.1%
-74.0969366 553
 
< 0.1%
-74.09 11
 
< 0.1%
-74.08896387 2
 
< 0.1%
-74.0887723 993
 
< 0.1%
-74.08801228 355
 
< 0.1%
-74.086701 23
 
< 0.1%
-74.08670068 3125
0.1%
-74.08593088 108
 
< 0.1%
ValueCountFrequency (%)
-73.94860294 1
 
< 0.1%
-73.9488 1
 
< 0.1%
-73.9488134 1
 
< 0.1%
-73.949373 1
 
< 0.1%
-73.94963041 1
 
< 0.1%
-73.949702 1
 
< 0.1%
-73.9503 2
< 0.1%
-73.950503 2
< 0.1%
-73.95068615 1
 
< 0.1%
-73.95095259 3
< 0.1%

bike_id
Real number (ℝ)

MISSING 

Distinct3110
Distinct (%)0.2%
Missing2483972
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean30498.736
Minimum14529
Maximum49985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 MiB
2024-01-21T15:18:20.521947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum14529
5-th percentile24475
Q126219
median29299
Q331695
95-th percentile44212
Maximum49985
Range35456
Interquartile range (IQR)5476

Descriptive statistics

Standard deviation6240.8602
Coefficient of variation (CV)0.20462684
Kurtosis0.39520877
Mean30498.736
Median Absolute Deviation (MAD)3049
Skewness1.1924102
Sum5.0956282 × 1010
Variance38948336
MonotonicityNot monotonic
2024-01-21T15:18:20.713476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26159 2688
 
0.1%
26260 2677
 
0.1%
26161 2659
 
0.1%
26236 2658
 
0.1%
26170 2653
 
0.1%
26306 2647
 
0.1%
26431 2629
 
0.1%
26192 2620
 
0.1%
26213 2617
 
0.1%
26286 2605
 
0.1%
Other values (3100) 1644314
39.6%
(Missing) 2483972
59.8%
ValueCountFrequency (%)
14529 3
 
< 0.1%
14531 43
 
< 0.1%
14536 19
 
< 0.1%
14552 10
 
< 0.1%
14556 34
 
< 0.1%
14578 3
 
< 0.1%
14585 100
< 0.1%
14598 57
 
< 0.1%
14607 175
< 0.1%
14632 1
 
< 0.1%
ValueCountFrequency (%)
49985 5
 
< 0.1%
49734 3
 
< 0.1%
49527 14
 
< 0.1%
49081 19
 
< 0.1%
49058 5
 
< 0.1%
48932 94
< 0.1%
48930 63
< 0.1%
48929 60
< 0.1%
48924 92
< 0.1%
48923 66
< 0.1%

user_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing487
Missing (%)< 0.1%
Memory size261.5 MiB
1
3162902 
0
991350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4154252
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3162902
76.1%
0 991350
 
23.9%
(Missing) 487
 
< 0.1%

Length

2024-01-21T15:18:20.881158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-21T15:18:21.017792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3162902
76.1%
0 991350
 
23.9%

Most occurring characters

ValueCountFrequency (%)
1 3162902
76.1%
0 991350
 
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4154252
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3162902
76.1%
0 991350
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4154252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3162902
76.1%
0 991350
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4154252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3162902
76.1%
0 991350
 
23.9%

birth_year
Real number (ℝ)

MISSING 

Distinct81
Distinct (%)< 0.1%
Missing2524704
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean1980.5907
Minimum1887
Maximum2004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 MiB
2024-01-21T15:18:21.170877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1887
5-th percentile1960
Q11974
median1983
Q31988
95-th percentile1994
Maximum2004
Range117
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.336333
Coefficient of variation (CV)0.0052188135
Kurtosis1.4461822
Mean1980.5907
Median Absolute Deviation (MAD)6
Skewness-0.89322602
Sum3.2284321 × 109
Variance106.83979
MonotonicityNot monotonic
2024-01-21T15:18:21.367329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1969 103535
 
2.5%
1989 83871
 
2.0%
1986 82971
 
2.0%
1988 81748
 
2.0%
1987 75175
 
1.8%
1984 72459
 
1.7%
1990 71014
 
1.7%
1983 66513
 
1.6%
1985 64494
 
1.6%
1981 62911
 
1.5%
Other values (71) 865344
 
20.8%
(Missing) 2524704
60.8%
ValueCountFrequency (%)
1887 70
 
< 0.1%
1888 260
< 0.1%
1889 4
 
< 0.1%
1900 65
 
< 0.1%
1901 1
 
< 0.1%
1904 19
 
< 0.1%
1918 2
 
< 0.1%
1920 1
 
< 0.1%
1923 3
 
< 0.1%
1930 4
 
< 0.1%
ValueCountFrequency (%)
2004 23
 
< 0.1%
2003 276
 
< 0.1%
2002 622
 
< 0.1%
2001 1405
 
< 0.1%
2000 2649
 
0.1%
1999 2737
 
0.1%
1998 5699
 
0.1%
1997 7657
 
0.2%
1996 16328
0.4%
1995 20409
0.5%

gender
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2483972
Missing (%)59.8%
Memory size260.0 MiB
1.0
1162335 
2.0
371323 
0.0
137109 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5012301
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1162335
28.0%
2.0 371323
 
8.9%
0.0 137109
 
3.3%
(Missing) 2483972
59.8%

Length

2024-01-21T15:18:21.523428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-21T15:18:21.648816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1162335
69.6%
2.0 371323
 
22.2%
0.0 137109
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 1807876
36.1%
. 1670767
33.3%
1 1162335
23.2%
2 371323
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3341534
66.7%
Other Punctuation 1670767
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1807876
54.1%
1 1162335
34.8%
2 371323
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 1670767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5012301
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1807876
36.1%
. 1670767
33.3%
1 1162335
23.2%
2 371323
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5012301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1807876
36.1%
. 1670767
33.3%
1 1162335
23.2%
2 371323
 
7.4%

ride_id
Text

MISSING 

Distinct2483972
Distinct (%)100.0%
Missing1670767
Missing (%)40.2%
Memory size255.6 MiB
2024-01-21T15:18:23.553016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters39743552
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2483972 ?
Unique (%)100.0%

Sample

1st row58EEE2950FFE01CE
2nd row1429D912C16EEE59
3rd rowFE9C5B74167CBCCD
4th rowB88D37626F000BBA
5th row2A75E47239553321
ValueCountFrequency (%)
58eee2950ffe01ce 1
 
< 0.1%
fe9c5b74167cbccd 1
 
< 0.1%
b88d37626f000bba 1
 
< 0.1%
9055e11e8d414319 1
 
< 0.1%
2a99fcafc6b1c7ce 1
 
< 0.1%
b31e3625915a88e1 1
 
< 0.1%
e0b99a4aaca77034 1
 
< 0.1%
bd52c7872af620e6 1
 
< 0.1%
20bd42adad2ca6a0 1
 
< 0.1%
23467b6f12bca65b 1
 
< 0.1%
Other values (2483962) 2483962
> 99.9%
2024-01-21T15:18:25.533560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2485763
 
6.3%
8 2485669
 
6.3%
2 2485302
 
6.3%
5 2485292
 
6.3%
A 2485246
 
6.3%
D 2484400
 
6.3%
1 2484339
 
6.3%
F 2484106
 
6.3%
E 2483925
 
6.2%
7 2483842
 
6.2%
Other values (6) 14895668
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24839670
62.5%
Uppercase Letter 14903882
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2485763
10.0%
8 2485669
10.0%
2 2485302
10.0%
5 2485292
10.0%
1 2484339
10.0%
7 2483842
10.0%
9 2482906
10.0%
4 2482782
10.0%
0 2481904
10.0%
6 2481871
10.0%
Uppercase Letter
ValueCountFrequency (%)
A 2485246
16.7%
D 2484400
16.7%
F 2484106
16.7%
E 2483925
16.7%
B 2483794
16.7%
C 2482411
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 24839670
62.5%
Latin 14903882
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2485763
10.0%
8 2485669
10.0%
2 2485302
10.0%
5 2485292
10.0%
1 2484339
10.0%
7 2483842
10.0%
9 2482906
10.0%
4 2482782
10.0%
0 2481904
10.0%
6 2481871
10.0%
Latin
ValueCountFrequency (%)
A 2485246
16.7%
D 2484400
16.7%
F 2484106
16.7%
E 2483925
16.7%
B 2483794
16.7%
C 2482411
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39743552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2485763
 
6.3%
8 2485669
 
6.3%
2 2485302
 
6.3%
5 2485292
 
6.3%
A 2485246
 
6.3%
D 2484400
 
6.3%
1 2484339
 
6.3%
F 2484106
 
6.3%
E 2483925
 
6.2%
7 2483842
 
6.2%
Other values (6) 14895668
37.5%

rideable_type
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1670767
Missing (%)40.2%
Memory size284.7 MiB
classic_bike
1826721 
electric_bike
520799 
docked_bike
 
136452

Length

Max length13
Median length12
Mean length12.154731
Min length11

Characters and Unicode

Total characters30192011
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdocked_bike
2nd rowdocked_bike
3rd rowdocked_bike
4th rowdocked_bike
5th rowdocked_bike

Common Values

ValueCountFrequency (%)
classic_bike 1826721
44.0%
electric_bike 520799
 
12.5%
docked_bike 136452
 
3.3%
(Missing) 1670767
40.2%

Length

2024-01-21T15:18:25.748799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-21T15:18:25.936159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
classic_bike 1826721
73.5%
electric_bike 520799
 
21.0%
docked_bike 136452
 
5.5%

Most occurring characters

ValueCountFrequency (%)
c 4831492
16.0%
i 4831492
16.0%
e 3662022
12.1%
s 3653442
12.1%
k 2620424
8.7%
b 2483972
8.2%
_ 2483972
8.2%
l 2347520
7.8%
a 1826721
 
6.1%
t 520799
 
1.7%
Other values (3) 930155
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27708039
91.8%
Connector Punctuation 2483972
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 4831492
17.4%
i 4831492
17.4%
e 3662022
13.2%
s 3653442
13.2%
k 2620424
9.5%
b 2483972
9.0%
l 2347520
8.5%
a 1826721
 
6.6%
t 520799
 
1.9%
r 520799
 
1.9%
Other values (2) 409356
 
1.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2483972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27708039
91.8%
Common 2483972
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 4831492
17.4%
i 4831492
17.4%
e 3662022
13.2%
s 3653442
13.2%
k 2620424
9.5%
b 2483972
9.0%
l 2347520
8.5%
a 1826721
 
6.6%
t 520799
 
1.9%
r 520799
 
1.9%
Other values (2) 409356
 
1.5%
Common
ValueCountFrequency (%)
_ 2483972
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30192011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4831492
16.0%
i 4831492
16.0%
e 3662022
12.1%
s 3653442
12.1%
k 2620424
8.7%
b 2483972
8.2%
_ 2483972
8.2%
l 2347520
7.8%
a 1826721
 
6.1%
t 520799
 
1.7%
Other values (3) 930155
 
3.1%

Interactions

2024-01-21T15:17:42.037960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:15.095206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:20.023030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:25.159999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:29.801778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:34.560544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:38.937752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:42.491785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:15.893588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:20.693016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:25.868764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:30.520413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:35.357745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:39.354099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:42.938197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:16.639475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:21.421146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:26.531357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:31.216839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:36.134756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:39.775037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:43.388062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:17.370589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:22.178348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:27.302903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:31.911844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:36.862383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:40.235619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:43.812147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:18.125059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:22.938612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:28.016845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:32.638262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:37.526439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:40.656757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:44.234043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:18.677875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:23.604458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:28.515074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:33.149564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:38.066019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:41.067045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:44.629581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:19.152542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:24.174584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:28.934213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:33.596738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:38.492399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-21T15:17:41.510467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-21T15:17:45.898420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-21T15:17:52.012680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trip_durationstart_timestop_timestart_station_idstart_station_namestart_station_latitudestart_station_longitudeend_station_idend_station_nameend_station_latitudeend_station_longitudebike_iduser_typebirth_yeargenderride_idrideable_type
0148.02017-01-01 00:21:322017-01-01 00:24:013276Marin Light Rail40.714584-74.0428173185City Hall40.717732-74.04384524575.011983.01.0NaNNaN
11283.02017-01-01 00:24:352017-01-01 00:45:583183Exchange Place40.716247-74.0334593198Heights Elevator40.748716-74.04044324723.011978.01.0NaNNaN
2372.02017-01-01 00:38:192017-01-01 00:44:313183Exchange Place40.716247-74.0334593211Newark Ave40.721525-74.04630524620.011989.01.0NaNNaN
31513.02017-01-01 00:38:372017-01-01 01:03:503194McGinley Square40.725340-74.0676223271Danforth Light Rail40.692640-74.08801224668.011961.01.0NaNNaN
4639.02017-01-01 01:47:522017-01-01 01:58:313183Exchange Place40.716247-74.0334593203Hamilton Park40.727596-74.04424726167.011993.01.0NaNNaN
5258.02017-01-01 01:56:292017-01-01 02:00:483186Grove St PATH40.719586-74.0431173270Jersey & 6th St40.725289-74.04557224604.011970.01.0NaNNaN
6663.02017-01-01 02:12:342017-01-01 02:23:383270Jersey & 6th St40.725289-74.0455723206Hilltop40.731169-74.05757424641.011978.01.0NaNNaN
7567.02017-01-01 02:15:522017-01-01 02:25:203192Liberty Light Rail40.711242-74.0557013213Van Vorst Park40.718489-74.04772724513.011970.01.0NaNNaN
8551.02017-01-01 02:16:032017-01-01 02:25:153192Liberty Light Rail40.711242-74.0557013213Van Vorst Park40.718489-74.04772724463.011967.02.0NaNNaN
9573.02017-01-01 02:22:172017-01-01 02:31:503212Christ Hospital40.734786-74.0504443225Baldwin at Montgomery40.723659-74.06419424486.011984.01.0NaNNaN
trip_durationstart_timestop_timestart_station_idstart_station_namestart_station_latitudestart_station_longitudeend_station_idend_station_nameend_station_latitudeend_station_longitudebike_iduser_typebirth_yeargenderride_idrideable_type
420796776.0NaTNaT3185City Hall40.717732-74.0438453185City Hall40.717732-74.04384544737.011976.01.0NaNNaN
4207968305.0NaTNaT3186Grove St PATH40.719586-74.0431173203Hamilton Park40.727596-74.04424742213.011996.01.0NaNNaN
420796999.0NaTNaT3185City Hall40.717732-74.0438453185City Hall40.717732-74.04384544347.011976.01.0NaNNaN
4207970492.0NaTNaT3195Sip Ave40.730897-74.0639133225Baldwin at Montgomery40.723659-74.06419446598.001998.01.0NaNNaN
4207971715.0NaTNaT3185City Hall40.717732-74.0438453269Brunswick & 6th40.726012-74.05038944737.011976.01.0NaNNaN
4207972270.0NaTNaT3207Oakland Ave40.737604-74.0524783640Journal Square40.733670-74.06250044744.011963.02.0NaNNaN
4207973400.0NaTNaT3209Brunswick St40.724176-74.0506563209Brunswick St40.724176-74.05065645345.011984.01.0NaNNaN
4207974206.0NaTNaT3195Sip Ave40.730897-74.0639133194McGinley Square40.725340-74.06762247019.011993.01.0NaNNaN
4207975216.0NaTNaT3195Sip Ave40.730897-74.0639133225Baldwin at Montgomery40.723659-74.06419442191.011966.01.0NaNNaN
4207976418.0NaTNaT3267Morris Canal40.712419-74.0385263186Grove St PATH40.719586-74.04311747255.011991.01.0NaNNaN

Duplicate rows

Most frequently occurring

trip_durationstart_timestop_timestart_station_namestart_station_latitudestart_station_longitudeend_station_nameend_station_latitudeend_station_longitudebike_iduser_typebirth_yeargenderride_idrideable_type# duplicates
834166.0NaTNaTMonmouth and 6th40.725685-74.048790Manila & 1st40.721651-74.04288429555.011993.01.0NaNNaN3
2229247.0NaTNaTDey St40.737711-74.0669215 Corners Library40.734961-74.05950345350.011978.01.0NaNNaN3
7306601.0NaTNaTNewport Pkwy40.728745-74.032108Essex Light Rail40.712774-74.03648642178.011980.01.0NaNNaN3
061.0NaTNaTBrunswick & 6th40.726012-74.050389Brunswick St40.724176-74.05065633887.011987.01.0NaNNaN2
161.0NaTNaTSip Ave40.730897-74.063913Sip Ave40.730897-74.06391342535.011977.02.0NaNNaN2
262.0NaTNaTCity Hall40.717732-74.043845City Hall40.717732-74.04384533859.011972.01.0NaNNaN2
362.0NaTNaTJersey & 6th St40.725289-74.045572Jersey & 3rd40.723332-74.04595333859.011977.01.0NaNNaN2
462.0NaTNaTWarren St40.721124-74.038051Warren St40.721124-74.03805142195.011970.02.0NaNNaN2
563.0NaTNaTGrove St PATH40.719586-74.043117City Hall40.717732-74.04384529588.011987.01.0NaNNaN2
663.0NaTNaTNewport Pkwy40.728745-74.032108Newport Pkwy40.728745-74.03210842460.011986.01.0NaNNaN2